Disentangling Recall and Reasoning in Transformer Models through Layer-wise Attention and Activation Analysis

ArXi:2510.03366v2 Announce Type: replace-cross Transformer-based language models excel at both recall (retrieving memorized facts) and reasoning (performing multi-step inference), but whether these abilities rely on distinct internal mechanisms remains unclear. Distinguishing recall from reasoning is crucial for predicting model generalization, designing targeted evaluations, and building safer interventions that affect one ability without disrupting the other.